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| Maschinelles Lernen-gestützter Matching-Schätzer× | Machine Learning-Augmented Doubly Robust Estimation (ML-DR)× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2006–2018 | 2018 |
| Urheber≠ | Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework) | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins |
| Typ≠ | Causal inference / nonparametric matching | Semiparametric causal estimator with ML nuisance |
| Wegweisende Quelle | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ |
| Aliasnamen | ML-augmented matching, ML matching estimator, high-dimensional matching estimator, data-adaptive matching estimator | ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails. | Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners. |
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